Out-of-distribution detection for deep neural networks with isolation forest and local outlier factor

HIGHLIGHTS

• who: Access and collaborators from the Department of Applied Physics and Electronics, Umeå University, Umeå, Sweden College of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China have published the paper: Out-Of-Distribution Detection for Deep Neural Networks with Isolation Forest and Local Outlier Factor, in the Journal: (JOURNAL)
• what: The authors propose to monitor one or more hidden layers of a DNN with two outlier/anomaly detection methods: Isolation Forest (IF) and Local Outlier Factor (LOF) . With these settings, the two DNNs used in the experiments have different last layer . . .
  

   

  

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